Dual Axis Chart
Dual Axis Chart SHARE THE ARTICLE ON Share on facebook Share on twitter Share on linkedin Table of Contents A dual axis chart also known
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(Along with a checklist to compare platforms)
Take a peek at our powerful survey features to design surveys that scale discoveries.
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We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
Find the best survey software for you!
(Along with a checklist to compare platforms)
Take a peek at our powerful survey features to design surveys that scale discoveries.
Explore VoxcoÂ
Need to map Voxco’s features & offerings? We can help!
We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
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In experimental research, units are assigned to an experimental group and to a treatment group. The treatment group, also known as the experimental group, receives the treatment being studied by the researcher. The control group, on the other hand, receives a placebo or no treatment at all. In a true experiment, units of the sample group are assigned to either group using random assignment. However, there are some studies that do not employ the use of random assignment. These studies follow a quasi-experimental research design.Â
The quasi-experimental design, just like a true experiment, aims to identify the cause-and-effect relationship between two variables; the independent and dependent variable. The only difference is that the quasi-experimental design employs non-random criteria while assigning subjects to groups.Â
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See Voxco survey software in action with a Free demo.
Let’s explore the most common types of quasi-experimental designs:
The regression discontinuity approach involves measuring the impact of the treatment, or independent variable, by applying a treatment assignment mechanism that is based on a continuous eligibility index that is variable with a continuous distribution.
Subjects are therefore selected to be a part of the treatment group based on whether their value on a predetermined numeric rating scale exceeds a designated threshold. Those who fall below that threshold, do not receive the treatment and will hence be part of the control group instead.Â
In the non-equivalent groups design, researchers select two groups; one is exposed to the treatment while the other is not. This is not considered random assignment as the researcher is working with pre-existing groups and not allocating subjects to either group based on random assignment. Although researchers aim to select two groups that are as similar as possible, we can never be sure that the groups are actually comparable. It is highly unlikely that the two groups would be as comparable as they would have been if they were created using random assignment. This design was named as the nonequivalent groups design for this very reason, as it’s likely that the two groups are not equivalent.Â
In both laboratory and field experiments, researchers generally determine how subjects are assigned to groups, whether that be through random or non-random criteria. However, in natural experiments, a naturally occurring external event or situation results in the random assignment of subjects to different groups. Therefore the factors that influence assignment are out of the control of investigators.
Although many natural experiments employ a method that resembles random assignment, they are still not considered to be true experiments because they utilize the observational method. Some situations in which natural experiments are appropriate and commonly used include policy changes, weather events, and natural disasters.Â
Quasi-experimental studies have lower internal validity than true experiments and also cannot establish a causal relationship between variables as effectively. So why do researchers use it?Â
There are certain situations where the use of a quasi-experimental design is more suited to the study. This is especially true for studies where it would be unethical to withhold treatment from a subject based on a random basis. In such situations, a quasi-experimental design can be utilized to avoid any ethical issues.
Additionally, another context in which a quasi-experimental design is more appropriate is when the true experiment design is not feasible. This could be due to the high expenses associated with true experiments. It could also be due to the fact that true experiments generally involve a lot of work to effectively design an experimental intervention for the threshold of subjects required to justify the research as a true experiment.
The following are a few advantages of utilizing a quasi-experimental research design:
The following are a few disadvantages of utilizing a quasi-experimental research design:
Explore all the survey question types possible on Voxco
Explore all the survey question types possible on Voxco
The quasi-experimental design, similar to true experiments, is a research design that aims to identify the causal relationship between an independent and dependent variable. However, unlike true experiments, quasi-experimental studies utilize non-random criteria while assigning subjects to groups.
Some common types of quasi-experimental designs are regression discontinuity, nonequivalent groups design, and natural experiments.
Some advantages of quasi-experimental studies are that, when compared to true experiments, they are less expensive and have higher external validity.
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